Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate metabolites

Identification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we test the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of co-varying features derived from Principal Component Analysis (PCA), the Iterative Signature Algorithm (ISA) and Averaged Correlation Profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. Based on the number and quality of the matches we concluded that both ISA and ACP can robustly identify about a dozen metabolites, half of which were shared, while PCA did not produce any signatures with robust matches.